VClass Engager: A Generative AI-Based System for Enhancing Student Engagement in Synchronous Online Classes

Student disengagement remains a major barrier to effective learning in synchronous online classrooms, where lack of interaction, limited feedback, and screen fatigue often contribute to passive participation. Despite the growing use of generative AI, there is a notable lack of empirical research investigating the application of generative AI in addressing engagement challenges in synchronous online sessions. This study introduces VClass Engager, a novel experimental system that utilizes generative AI to foster student participation and deliver instant, personalized feedback during live virtual sessions. The system integrates several features, including instant analysis of student answers to chat questions, real-time AI-generated feedback, and a leaderboard that displays students’ cumulative scores to promote sustained engagement. To assess its effectiveness, the system was evaluated across multiple courses. Engagement was measured by tracking participation in three in-class formative questions, and response quality was analyzed using a cumulative link mixed model (CLMM). In addition, a post-session survey captured students’ perceptions regarding usability, motivational impact, and feedback quality. Results demonstrated statistically significant increases in student participation and response quality in sessions using VClass Engager compared to baseline sessions. Survey responses revealed high levels of satisfaction with the system’s ease of use and motivational aspects. By combining AI and gamification, this study provides early empirical evidence for a promising approach to enhancing engagement in synchronous online learning.